Muse Spark 1.1 vs Grok 4.20 Multi-Agent: which should you choose in 2026?
Muse Spark 1.1 (by Meta) and Grok 4.20 Multi-Agent (by xAI) are compared below. Here is how they stack up on benchmarks, price, and capabilities, and which one to pick in 2026.
There are not enough shared, protocol-compatible benchmark results to declare a performance leader.
At least one model uses context-dependent token-pricing tiers, so the published base rates do not support an unconditional blended price comparison.
Grok 4.20 Multi-Agent has the larger context window (2,000,000 tokens vs 1,048,576 tokens).
Choose Muse Spark 1.1 if…
- • your own prompt tests favor its output; shared comparable evidence does not identify a unique advantage
Choose Grok 4.20 Multi-Agent if…
- • you work with longer documents, transcripts, or codebases
The benchmark count includes only results measured with a matching benchmark version and protocol. Arena scores are shown separately. Missing, preliminary, and incompatible data is not treated as a controlled win. Published point-score comparisons are labeled separately when protocol details are incomplete. For text-output models, the verdict also compares token pricing and context windows.
Performance benchmarks
Every value links to its source. A dash means that no reviewed result is available for that exact model and protocol.
| Benchmark | Muse Spark 1.1 | Grok 4.20 Multi-Agent |
|---|---|---|
A 657-task benchmark of multi-step work across simulated SaaS applications in six business domains. The Artificial Analysis protocol reports a guardrail-aware score and is distinct from both the public Zapier split and the unrelated dynamic AutoBench framework. Guardrail-aware score | 42.8% Artificial Analysis | — |
A benchmark of difficult, verifiable information-seeking questions designed to measure an agent's ability to locate hard-to-find facts through web browsing. Accuracy | — | — |
Finance Agent v2 A Vals AI benchmark with 927 expert-reviewed questions modeling the work of entry-level financial analysts. Task score | 57.2% Vals AI | — |
JobBench A professional tool-use benchmark with 65 tasks spanning 35 white-collar occupations. Task score | 54.7% JobBench | — |
MCP Atlas A scaled tool-use benchmark with 1,000 multi-step tasks across 36 real MCP servers and 220 tools. Tasks completed | 88.1%±1.9 Scale AI | — |
A verified computer-use benchmark in which multimodal agents operate desktop applications and are graded from the resulting environment state. Mean task reward | 80.8% Meta report | — |
A long-horizon software-engineering benchmark with 113 original tasks graded by hand-written tests. Pass@1 | 53.0%±3.0 DataCurve | — |
A contamination-resistant software-engineering benchmark with long-horizon tasks across multiple programming languages. Public and private splits are distinct protocols. Resolved | 61.5%±3.1 Scale AI | — |
A human-validated subset of real GitHub issues used to measure whether a coding agent can produce repository patches that resolve the associated tests. Resolved | — | — |
Version 2.1 of the benchmark for completing realistic tasks in terminal environments. Harness, resource limits, and attempt count are part of the protocol. Mean task success | 80.0% Meta report | — |
A dynamic LLM evaluation framework in which models generate questions, answer them, and participate in reciprocal peer assessment. AutoBench is distinct from Zapier's AutomationBench. Weighted peer-assessment score | — | — |
The highest-quality subset of Graduate-Level Google-Proof Q&A, designed to test expert-level scientific reasoning in biology, physics, and chemistry. Accuracy | — | — |
A 2,500-question expert-level benchmark spanning dozens of academic fields. Tool-assisted and no-tools results are separate protocols and must not be merged. Accuracy | 62.1% Meta report | — |
A contamination-resistant benchmark refreshed on a fixed release cadence. Scores from different LiveBench releases must never be compared as the same protocol. Mean of category averages | 75.3% LiveBench | — |
BabyVision A 388-question visual-understanding benchmark covering fine-grained discrimination, spatial perception, tracking, and pattern recognition. Accuracy | 76.3% Meta report | — |
The chart-reasoning portion of CharXiv, evaluating visual and mathematical reasoning over scientific figures. Accuracy | 88.4% Meta report | — |
| Arena preference scores | ||
Human preference score | 1491 ±5 | — |
Human preference score for code and web development | 1539 ±9 | — |
Benchmark sources
- Arena AI leaderboard
- AutomationBench-AA: Agentic SaaS Workflow Benchmark
- Finance Agent v2 Leaderboard
- JobBench Leaderboard
- MCP Atlas Leaderboard
- Muse Spark 1.1 Evaluation Report
- DeepSWE 1.1 Leaderboard
- SWE-Bench Pro Public Leaderboard
- LiveBench 2026-06-25 Leaderboard
Reviewed evidence last updated 2026-08-09. Arena data last refreshed Aug 21, 2026.
Pricing, capabilities, and model facts
| Feature | Muse Spark 1.1 | Grok 4.20 Multi-Agent |
|---|---|---|
| Context & model facts | ||
| Developer | Meta | xAI |
| API provider | Meta | SpaceXAI |
| Input context | 1,048,576 tokens | 2,000,000 tokens |
| Maximum output | 131,072 tokens | — |
| Released | Jul 9, 2026 | — |
| Added to Writingmate | Jul 16, 2026 | Mar 31, 2026 |
| License | Proprietary | Not available |
| Knowledge cutoff | Not disclosed | 2025-09-01 |
| Capabilities | ||
| Inputs | Text, Image, Video, File, Audio | Text, Image, File |
| Outputs | Text | Text |
| Provider endpoint accepts tool parameters | Yes | No |
| Reasoning | Yes | Yes |
| Vision | Yes | Yes |
| Image Generation | No | No |
| Video Generation | No | No |
| API pricing | ||
| Base input (per 1M tokens) | $1.25 | $1.25 |
| Base output (per 1M tokens) | $4.25 | $2.50 |
| Higher-context pricing tiers | Base rate only |
|
| Price-comparison caveat | At least one model changes token rates above a prompt-token threshold. Base rates are shown, but an unconditional blended price ratio would not be like-for-like. | |
| API performance | ||
| p95 latency | Not measured | Not measured |
| Output throughput | Not measured | Not measured |
| Writingmate shows API performance only when both models have enough observations from the same measurement window, prompt profile, and provider. Third-party latency values are not copied into this table. | ||
Data sources
- Muse Spark 1.1 Evaluation Report
- Meta Model API provider documentation
- Writingmate model catalog (pricing, limits, and availability)
Catalog data last updated Jul 16, 2026.
Why Pay for Multiple Subscriptions?
Comparing Muse Spark 1.1 from Meta with Grok 4.20 Multi-Agent from xAI? Instead of managing separate API keys and subscriptions, get both with Writingmate.
| Plan | Price | Muse Spark 1.1 | Grok 4.20 Multi-Agent | AI Images | AI Video |
|---|---|---|---|---|---|
Writingmate Pro Most popular | $20/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | Sora 2, VEO 3.1 |
Writingmate Ultimate Power users | $60/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | Sora 2, VEO 3.1 |
Muse Spark 1.1 vs Grok 4.20 Multi-Agent FAQ
Which is better, Muse Spark 1.1 or Grok 4.20 Multi-Agent?
There are not enough shared, protocol-compatible benchmark results to declare a performance leader. At least one model uses context-dependent token-pricing tiers, so the published base rates do not support an unconditional blended price comparison. Grok 4.20 Multi-Agent has the larger context window (2,000,000 tokens vs 1,048,576 tokens).
Which model is cheaper to use through an API?
At least one model uses context-dependent token-pricing tiers, so the published base rates do not support an unconditional blended price comparison.
Which model supports more context?
Grok 4.20 Multi-Agent has the larger context window (2,000,000 tokens vs 1,048,576 tokens).
Can I switch between Muse Spark 1.1 and Grok 4.20 Multi-Agent?
Yes. Use the model selector on this page to open any current Writingmate model comparison. You can also run the same prompt with both models in Writingmate.
Related comparisons
More Muse Spark 1.1 comparisons
GPT-5.3-Codex
OpenAI
GPT-5.3-Codex is OpenAI’s most advanced agentic coding model, combining the frontier software engineering performance of GPT-5.2-Codex with the broader reasoning and professional knowledge capabilities of GPT-5.2.
Gemini 3.7 Flash
Gemini 3.7 Flash is a multimodal model from Google for fast agentic workflows, coding, and complex multi-step reasoning.
Gemini 3.6 Flash
Gemini 3.6 Flash is a high-efficiency model from Google for coding, agentic workflows, and web and app development.
Gemini 3.5 Flash Lite
Gemini 3.5 Flash Lite is a high-efficiency model from Google with upgraded agentic capabilities.
MiMo-V2.5
Xiaomi
MiMo-V2.5 is a native omnimodal model by Xiaomi.
Nova 2 Lite
Amazon
Nova 2 Lite is a fast, cost-effective reasoning model for everyday workloads that can process text, images, and videos to generate text.
More Grok 4.20 Multi-Agent comparisons
Muse Spark 1.2 Contributor
Meta
Muse Spark 1.2 contributor tier is a reasoning model from Meta designed for developers who want to start building at an even lower cost.
Gemini 3.7 Flash
Gemini 3.7 Flash is a multimodal model from Google for fast agentic workflows, coding, and complex multi-step reasoning.
Sakana Namazu
Sakana AI
Sakana Namazu is a Japanese-specialized reasoning model from Sakana AI, based on Kimi K2.6 with additional training for Japanese language and business contexts.
Muse Spark 1.2
Meta
Muse Spark 1.2 is a reasoning model from Meta, designed for complex agentic tasks.
Claude Opus 5
Anthropic
Claude Opus 5 is Anthropic’s flagship model for demanding reasoning, coding, and long-horizon agentic work.
Gemini 3.6 Flash
Gemini 3.6 Flash is a high-efficiency model from Google for coding, agentic workflows, and web and app development.